The proposed framework is implemented using TensorFlow 1.14 and Keras 2.0.0 with Python 3.7.4 on the Anaconda platform. Some of the utility functions are also implemented using MATLAB R2020a. The proposed framework's training was conducted in two phases where the candidate classification model minimized the \(L_{IL}\)  loss function in each iteration. The number of epochs (in each training increment) was 20 (and the number of cycles in each epoch varies as per each dataset). Also, during each training increment, we fed the candidate network with around 20% of the original training data (where 10% were used for the distillation process and the remaining 10% were used to learn the newly added classes). Apart from this, we used ADADELTA [1]} as an optimizer, and the training was conducted on the machine with a Core i7-9750H@2.6 GHz processor, 32GB DDR4 RAM, and NVIDIA RTX 2080 Max-Q GPU with cuDNN v7.5 and a CUDA Toolkit 10.1.243.
